license: cc-by-4.0
pretty_name: Starlink Constellation Fleet Data
language:
- en
description: >-
Daily health snapshots of the SpaceX Starlink constellation, derived from
CelesTrak GP (General Perturbations) data. Tracks satellite count, orbital
shells, operational status, and ISL capability acro
task_categories:
- time-series-forecasting
- tabular-classification
tags:
- space
- starlink
- satellites
- orbital-mechanics
- tle
- spacex
- constellation
- open-data
- norad
- leo
- mega-constellation
- tabular-data
- parquet
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/daily_snapshots.parquet
default: true
Starlink Constellation Fleet Data
Credit: NASA
Part of a dataset collection on Hugging Face.
Dataset description
Daily health snapshots of the SpaceX Starlink constellation, derived from CelesTrak GP (General Perturbations) data. Tracks satellite count, orbital shells, operational status, and ISL capability across five inclination-based shells.
Starlink is the largest satellite constellation ever built, representing a fundamental shift in how broadband internet is delivered globally. SpaceX deploys satellites into five distinct orbital shells, each defined by its inclination and target altitude. The constellation operates in low Earth orbit (LEO) at altitudes between 328 km and 570 km, where orbital periods of roughly 90 minutes mean each satellite circles the Earth about 16 times per day.
Understanding constellation health requires tracking each satellite through its lifecycle: initial deployment to a parking orbit, orbit raising via Hall-effect ion thrusters, operational service at target altitude, and eventual controlled deorbit. The mean motion derivative serves as a reliable proxy for whether a satellite is actively thrusting upward or decaying. The inter-satellite laser link (ISL) capability, rolled out starting in 2022, enables direct satellite-to-satellite routing without ground relay.
This dataset is suitable for time-series forecasting, tabular classification tasks.
Schema
| Column | Type | Description | Sample | Null % |
|---|---|---|---|---|
date |
datetime64[ns] | UTC date of the daily snapshot; one set of rows per date per shell | 2019-05-25 00:00:00 | 0.0% |
shell_id |
int64 | Integer shell identifier (0-4); maps to inclination bands: 0=33 deg, 1=43 deg, 2=53 deg, 3=70 deg, 4=97.6 deg | 2 | 0.0% |
total_count |
int64 | Total number of Starlink objects tracked in this shell on this date, including all statuses | 3 | 0.0% |
operational_count |
int64 | Satellites with perigee altitude within the shell's operational band (typically 460-570 km depending on shell) | 0 | 0.0% |
raising_count |
int64 | Satellites currently maneuvering toward their target shell altitude via Hall-effect ion thrusters | 1 | 0.0% |
deorbiting_count |
int64 | Satellites in active controlled deorbit below their shell band with strong positive mean_motion_dot, or below 300 km | 0 | 0.0% |
reentered_count |
float64 | 0.0 | 6.1% | |
isl_operational_count |
int64 | Operational satellites equipped with inter-satellite laser links (ISL); ISL-capable units were deployed from 2022 onward depending on shell | 0 | 0.0% |
avg_altitude |
float64 | 441.08666666666664 | 6.1% | |
min_altitude |
float64 | 439.58 | 6.1% | |
max_altitude |
float64 | 443.58 | 6.1% | |
new_launches |
int64 | Reserved for future use; currently always 0 | 3 | 0.0% |
anomalous_count |
float64 | 0.0 | 6.1% | |
shell_name |
str | Human-readable shell label encoding inclination and target altitude, e.g. 'Shell 3 (53 deg / 550km)' | Shell 3 (53° / 550km) | 0.0% |
Quick stats
- 10,169 Starlink satellites tracked
- 8,827 operational, 1,297 raising, 44 deorbiting
- 9,205 ISL-capable satellites
- 7,323 daily snapshot rows (2019-05-25 to 2026-04-21)
Usage
from datasets import load_dataset
ds = load_dataset("juliensimon/starlink-fleet-data", split="train")
df = ds.to_pandas()
from datasets import load_dataset
ds = load_dataset("juliensimon/starlink-fleet-data", "daily_snapshots", split="train")
df = ds.to_pandas()
# Constellation growth over time
growth = df.groupby("date")["operational_count"].sum()
print(growth.tail(10))
# Per-shell fill rates
latest = df[df["date"] == df["date"].max()]
for _, row in latest.iterrows():
print(f"{row['shell_name']}: {row['operational_count']} / {row['total_count']}")
# Plot operational growth by shell
import matplotlib.pyplot as plt
for sid in sorted(df["shell_id"].unique()):
shell = df[df["shell_id"] == sid]
plt.plot(shell["date"], shell["operational_count"], label=shell["shell_name"].iloc[0])
plt.xlabel("Date")
plt.ylabel("Operational Satellites")
plt.title("Starlink Constellation Growth by Shell")
plt.legend()
plt.show()
Data source
Update schedule
Daily at 08:00 UTC via GitHub Actions
Related datasets
If you find this dataset useful, please consider giving it a like on Hugging Face. It helps others discover it.
About the author
Created by Julien Simon — AI Operating Partner at Fortino Capital. Part of the Space Datasets collection.
Citation
@dataset{starlink_fleet_data,
title = {Starlink Constellation Fleet Data},
author = {juliensimon},
year = {2026},
url = {https://huggingface.co/datasets/juliensimon/starlink-fleet-data},
publisher = {Hugging Face}
}